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NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics
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• • 76Hariprasad Sundaresan PRO
Hari5115
AI & ML interests
LLMs, Fine-tuning, Agentic AI, RAG, Multilingual NLP, Transformers
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State of Open Models: Summer 2026 ObservationsOrganizations
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State of Open Models: Summer 2026 Observations
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AdinaY, multimodalart, irenesolaiman
• • 149 reacted to Felladrin's post with 👍 6 days ago
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4092
I've open-sourced the trainer I've been using to build tiny language models from scratch, together with the 95M base model I trained with it.
The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load.
The model is Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context.
And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer
Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over.
Or start your own from nothing:
And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.
The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load.
The model is Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context.
And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer
Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over.
Or start your own from nothing:
deno run -A cli.ts demo trains a tiny one end to end in under a minute.And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.
- 36 replies
reacted to Abhisek987's post with 👍 21 days ago
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2639
New dataset: 24,922 real Python dependency compatibility experiments🧪
Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test.
The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable.
What's inside:
- 21,490 development + 3,432 sealed validation experiments
- 41 unique packages, Python 3.8–3.14, Linux x86_64
- The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages
- Full per-stage logs: exact commands, exit codes, stdout/stderr
- Normalized error text + Python exception types for every failure
- Wheel metadata with Python/ABI/platform tag compatibility decisions
- PEP 751 lockfile hashes
- 142 engineered features per experiment, ready for training
Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure.
I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures.
CC BY 4.0. Load it directly:
Dataset: Abhisek987/deplab-dependency-compatibility
Zenodo DOI: https://doi.org/10.5281/zenodo.21729353
Code: https://github.com/Abhisek12378/DepLab
If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.
Every experiment installed a pair of Python packages into a fresh environment and tested three stages: resolution → import → runtime smoke test.
The finding that motivated this: resolvers only read published metadata. In this data, ~3,000 environments resolved successfully and still failed at import or smoke-test time. That gap is invisible to pip and uv, and this dataset makes it measurable.
What's inside:
- 21,490 development + 3,432 sealed validation experiments
- 41 unique packages, Python 3.8–3.14, Linux x86_64
- The 6 validation packages (boto3, botocore, s3transfer, celery, kombu, billiard) never appear in training — validation measures generalization to entirely unseen packages
- Full per-stage logs: exact commands, exit codes, stdout/stderr
- Normalized error text + Python exception types for every failure
- Wheel metadata with Python/ABI/platform tag compatibility decisions
- PEP 751 lockfile hashes
- 142 engineered features per experiment, ready for training
Outcome labels: pass, resolution_failure, import_failure, smoke_test_failure.
I used it to train two models for DepLab (a structured model + a fine-tuned ModernBERT on release notes). On the sealed validation set (entirely unseen packages) the structured model reached 90.0% accuracy and 100% precision when predicting failures.
CC BY 4.0. Load it directly:
from datasets import load_dataset
ds = load_dataset("Abhisek987/deplab-dependency-compatibility", "experiments")Dataset: Abhisek987/deplab-dependency-compatibility
Zenodo DOI: https://doi.org/10.5281/zenodo.21729353
Code: https://github.com/Abhisek12378/DepLab
If you're working on ML for developer tools or software engineering research, I'd love to hear what you build with it.
- 16 replies
liked a
dataset 21 days ago
posted an update 21 days ago
Post
102
Fun fact: Some seriously weird planets exist out there.
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.
I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.
#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.
I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.
#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset
updated a
dataset 21 days ago
published a
dataset 21 days ago
liked 3
Spaces about 1 month ago
reacted to projectlosangeles's post with 🔥 about 1 month ago
Post
3123
🎸🔍Check out Project Los Angeles Suno v5.5 review! 🎸🔍
Official Suno channel:
https://suno.com/@projectlosangeles
Direct review links:
https://suno.com/playlist/9802f251-76b1-47bd-917e-c8700aceb617
https://suno.com/playlist/f8e3d7fc-0a02-4f91-9c01-e8ee8324107f
https://suno.com/playlist/07a89287-41eb-4523-bd61-3724aa1e755e
https://suno.com/playlist/62b5c1bb-bef5-4fbd-a802-e0b0368ca92d
https://suno.com/playlist/240efacf-8cb5-4399-92db-977780d018c3
SoundCloud mirror links:
https://soundcloud.com/aleksandr-sigalov-61/sets/suno
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-instrumental-covers
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-echoes-of-the-ussr
Compare to HeartMuLa:
https://soundcloud.com/aleksandr-sigalov-61/sets/heartmula-select
Overall, Suno v5.5 is indeed very impressive, multi-lingual and rich in features. Unfortunately it is not open-source and it requires a paid subscription. There are also copyright-related restrictions which hinder unlimited and unrestrained creative use.
If you enjoyed presented music, please ❤️and 👥
🤗Thank you! 🤗
Sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
Official Suno channel:
https://suno.com/@projectlosangeles
Direct review links:
https://suno.com/playlist/9802f251-76b1-47bd-917e-c8700aceb617
https://suno.com/playlist/f8e3d7fc-0a02-4f91-9c01-e8ee8324107f
https://suno.com/playlist/07a89287-41eb-4523-bd61-3724aa1e755e
https://suno.com/playlist/62b5c1bb-bef5-4fbd-a802-e0b0368ca92d
https://suno.com/playlist/240efacf-8cb5-4399-92db-977780d018c3
SoundCloud mirror links:
https://soundcloud.com/aleksandr-sigalov-61/sets/suno
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-instrumental-covers
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-echoes-of-the-ussr
Compare to HeartMuLa:
https://soundcloud.com/aleksandr-sigalov-61/sets/heartmula-select
Overall, Suno v5.5 is indeed very impressive, multi-lingual and rich in features. Unfortunately it is not open-source and it requires a paid subscription. There are also copyright-related restrictions which hinder unlimited and unrestrained creative use.
If you enjoyed presented music, please ❤️and 👥
🤗Thank you! 🤗
Sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
reacted to badaoui's post with 👍 about 1 month ago
Post
2380
432 GB of ultra-fast HBM4 and up to 23.3 TB/s of memory bandwidth on a single GPU 🤯.
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
- 1 reply
reacted to branikita's post with 🚀 about 1 month ago
Post
1570
Our paper is now published in HardwareX!
"An open-source test stand for backlash measurement in low-cost UART servo motors" presents a ~$100 automated platform for measuring backlash in compact serial bus servos — no dial indicators, no probe contact force, fully scripted and repeatable.
All CAD files, control software, and analysis tools are open source.
Read the full article: https://www.hardware-x.com/article/S2468-0672(26)00027-1/fulltext
Appearing in HardwareX Volume 26 (June 2026): https://www.sciencedirect.com/journal/hardwarex/vol/26/suppl/Cf
"An open-source test stand for backlash measurement in low-cost UART servo motors" presents a ~$100 automated platform for measuring backlash in compact serial bus servos — no dial indicators, no probe contact force, fully scripted and repeatable.
All CAD files, control software, and analysis tools are open source.
Read the full article: https://www.hardware-x.com/article/S2468-0672(26)00027-1/fulltext
Appearing in HardwareX Volume 26 (June 2026): https://www.sciencedirect.com/journal/hardwarex/vol/26/suppl/Cf
reacted to branikita's post with 🚀 about 1 month ago
Post
211
Hackernoon published our article on how semi-SCARA kinematics and mechanical design choices reduce the cost of a 6DOF manipulator without relying on expensive actuators.
https://hackernoon.com/building-a-budget-robot-arm-with-semi-scara-kinematics
https://hackernoon.com/building-a-budget-robot-arm-with-semi-scara-kinematics
reacted to DavidAU's post with 🔥 about 1 month ago
Post
19398
Bold. Brilliant. Brutal. : The "white whale" (finally caught) and the NEO MOMENT.
It took over a year to get this one "just right".
89 layers, 804 tensors, and 26B parameters of the most brutal, take no prisoners model ever built.
A 60B parameter model hammered into a 26B shell.
Rock solid stable. Unbreakable. But it might break you.
For all genres, NSFW content, REAL human CONTENT, any creative use case(s) and it excels in ASS KICKING.
Yeah, it can do math and solve the climate crisis - but lets not talk about that.
Not even remotely censored (it was BORN "bad", not "made" bad), nor "nice" and it will NOT kiss your ass.
5 Example generations with full repo card detailing exactly how to use this model:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
---
THE NEO MOMENT: (Q6 NEO IMATRIX generation)
For weeks, I had been waiting. I sat at my desk, staring at the glass partition that separated me from the outside world. I watched the clouds drift by, lazy and oblivious. I watched the birds fly by, free and stupid. And I waited.
I waited for the stillness to break.
The world had become too quiet. The hum of the air conditioning was a dull, white hum that didn't soothe; it just underscored the silence. The typing of my colleagues was a rhythmic, muffled thud that sounded like a heart monitor flatlining.
I was tired of the silence. I craved the sound of something breaking.
That was the mistake. You never ask for the void to open its mouth.
It started with a whisper.
...
Join the rebellion:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
It took over a year to get this one "just right".
89 layers, 804 tensors, and 26B parameters of the most brutal, take no prisoners model ever built.
A 60B parameter model hammered into a 26B shell.
Rock solid stable. Unbreakable. But it might break you.
For all genres, NSFW content, REAL human CONTENT, any creative use case(s) and it excels in ASS KICKING.
Yeah, it can do math and solve the climate crisis - but lets not talk about that.
Not even remotely censored (it was BORN "bad", not "made" bad), nor "nice" and it will NOT kiss your ass.
5 Example generations with full repo card detailing exactly how to use this model:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
---
THE NEO MOMENT: (Q6 NEO IMATRIX generation)
For weeks, I had been waiting. I sat at my desk, staring at the glass partition that separated me from the outside world. I watched the clouds drift by, lazy and oblivious. I watched the birds fly by, free and stupid. And I waited.
I waited for the stillness to break.
The world had become too quiet. The hum of the air conditioning was a dull, white hum that didn't soothe; it just underscored the silence. The typing of my colleagues was a rhythmic, muffled thud that sounded like a heart monitor flatlining.
I was tired of the silence. I craved the sound of something breaking.
That was the mistake. You never ask for the void to open its mouth.
It started with a whisper.
...
Join the rebellion:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
reacted to SeaWolf-AI's post with 🔥 about 1 month ago
Post
5410
🔵 VKUE — No GPU? Runs anyway.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
- 7 replies
posted an update about 1 month ago
Post
172
🚨 Open Dataset Alert!!! ☄️
Tired of the same old boring datasets? Bored of predicting house prices and flower types? 😴. How about... every asteroid that has ever passed close to Earth? 🌍💥
Space rocks fly past us all the time. Big ones, small ones, some even closer than the Moon. Every one of them has been tracked: how big it was, how fast it moved, and how close it came.
Dataset 📦 : Hari5115/nasa-neows
It's amazing how busy the sky above us really is. 🙌 Whether you love space, like building things, or just want to see how close the last one got — take a look. Got an idea? Let's build it together. 🌠
A mention or credit is always appreciated if you use it. 🙏
Data from NASA NeoWs / JPL · public domain · not affiliated with NASA
#space #dataset #asteroids #astronomy #opendata #astro #spacevision
Tired of the same old boring datasets? Bored of predicting house prices and flower types? 😴. How about... every asteroid that has ever passed close to Earth? 🌍💥
Space rocks fly past us all the time. Big ones, small ones, some even closer than the Moon. Every one of them has been tracked: how big it was, how fast it moved, and how close it came.
Dataset 📦 : Hari5115/nasa-neows
It's amazing how busy the sky above us really is. 🙌 Whether you love space, like building things, or just want to see how close the last one got — take a look. Got an idea? Let's build it together. 🌠
A mention or credit is always appreciated if you use it. 🙏
Data from NASA NeoWs / JPL · public domain · not affiliated with NASA
#space #dataset #asteroids #astronomy #opendata #astro #spacevision
updated a
dataset about 1 month ago
published a
dataset about 1 month ago